Bibliographic record
Abstract
Professional road cycling in general and the Tour de France in particular have a tarnished \nreputation as far as the illegal and illegitimate use of performance enhancing \ndrugs is concerned. Numerous positive dope tests each year are, for some, testament \nto the insidious corruptness of cyclists, their entourage, and the practice community. \nFor others, it attests to both the strength of the commitment to drug free sport and the \nrigor of the processes implemented to achieve it. In a recent interview on British TV, \nMark Cavendish a winner of 6 Tour de France stages in 2009, claimed that no other \nsport was as committed to clean competition as road cycling1. Although standard \nantidoping arguments have been presented, discussed, and widely rehearsed in the \nliterature, consensus on the matter has not been reached neither in the community of \nsports ethicists nor, as I will suggest, in the practice community of elite road cyclists. \nIn this paper I explore a possible defense of doping in elite cycling which requires us \nto think carefully about common assumptions about both the nature and purpose of \ndoping. In particular I examine the way in which both realists and antirealists might \ndeal with a particular prodoping argument.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.133 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".